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      Devices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review

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          Abstract

          Background

          It is well documented that meeting the guideline levels (150 minutes per week) of moderate-to-vigorous physical activity (PA) is protective against chronic disease. Conversely, emerging evidence indicates the deleterious effects of prolonged sitting. Therefore, there is a need to change both behaviors. Self-monitoring of behavior is one of the most robust behavior-change techniques available. The growing number of technologies in the consumer electronics sector provides a unique opportunity for individuals to self-monitor their behavior.

          Objective

          The aim of this study is to review the characteristics and measurement properties of currently available self-monitoring devices for sedentary time and/or PA.

          Methods

          To identify technologies, four scientific databases were systematically searched using key terms related to behavior, measurement, and population. Articles published through October 2015 were identified. To identify technologies from the consumer electronic sector, systematic searches of three Internet search engines were also performed through to October 1, 2015.

          Results

          The initial database searches identified 46 devices and the Internet search engines identified 100 devices yielding a total of 146 technologies. Of these, 64 were further removed because they were currently unavailable for purchase or there was no evidence that they were designed for, had been used in, or could readily be modified for self-monitoring purposes. The remaining 82 technologies were included in this review (73 devices self-monitored PA, 9 devices self-monitored sedentary time). Of the 82 devices included, this review identified no published articles in which these devices were used for the purpose of self-monitoring PA and/or sedentary behavior; however, a number of technologies were found via Internet searches that matched the criteria for self-monitoring and provided immediate feedback on PA (ActiGraph Link, Microsoft Band, and Garmin Vivofit) and sedentary time (activPAL VT, the Lumo Back, and Darma).

          Conclusions

          There are a large number of devices that self-monitor PA; however, there is a greater need for the development of tools to self-monitor sedentary time. The novelty of these devices means they have yet to be used in behavior change interventions, although the growing field of wearable technology may facilitate this to change.

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          Most cited references94

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          Breaks in sedentary time: beneficial associations with metabolic risk.

          Total sedentary (absence of whole-body movement) time is associated with obesity, abnormal glucose metabolism, and the metabolic syndrome. In addition to the effects of total sedentary time, the manner in which it is accumulated may also be important. We examined the association of breaks in objectively measured sedentary time with biological markers of metabolic risk. Participants (n = 168, mean age 53.4 years) for this cross-sectional study were recruited from the 2004-2005 Australian Diabetes, Obesity and Lifestyle study. Sedentary time was measured by an accelerometer (counts/minute(-1) or = 100) was considered a break. Fasting plasma glucose, 2-h plasma glucose, serum triglycerides, HDL cholesterol, weight, height, waist circumference, and resting blood pressure were measured. MatLab was used to derive the breaks variable; SPSS was used for the statistical analysis. Independent of total sedentary time and moderate-to-vigorous intensity activity time, increased breaks in sedentary time were beneficially associated with waist circumference (standardized beta = -0.16, 95% CI -0.31 to -0.02, P = 0.026), BMI (beta = -0.19, -0.35 to -0.02, P = 0.026), triglycerides (beta = -0.18, -0.34 to -0.02, P = 0.029), and 2-h plasma glucose (beta = -0.18, -0.34 to -0.02, P = 0.025). This study provides evidence of the importance of avoiding prolonged uninterrupted periods of sedentary (primarily sitting) time. These findings suggest new public health recommendations regarding breaking up sedentary time that are complementary to those for physical activity.
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            Validation of wearable monitors for assessing sedentary behavior.

            A primary barrier to elucidating the association between sedentary behavior (SB) and health outcomes is the lack of valid monitors to assess SB in a free-living environment. The purpose of this study was to examine the validity of commercially available monitors to assess SB. Twenty overweight (mean ± SD: body mass index = 33.7 ± 5.7 kg·m(-2)) inactive, office workers age 46.5 ± 10.7 yr were directly observed for two 6-h periods while wearing an activPAL (AP) and an ActiGraph GT3X (AG). During the second observation, participants were instructed to reduce sitting time. We assessed the validity of the commonly used cut point of 100 counts per minute (AG100) and several additional AG cut points for defining SB. We used direct observation (DO) using focal sampling with duration coding to record either sedentary (sitting/lying) or nonsedentary behavior. The accuracy and precision of the monitors and the sensitivity of the monitors to detect reductions in sitting time were assessed using mixed-model repeated-measures analyses. On average, the AP and the AG100 underestimated sitting time by 2.8% and 4.9%, respectively. The correlation between the AP and DO was R2 = 0.94, and the AG100 and DO sedentary minutes was R2 = 0.39. Only the AP was able to detect reductions in sitting time. The AG 150-counts-per-minute threshold demonstrated the lowest bias (1.8%) of the AG cut points. The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.
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              The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study

              Background Technological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors. Methods 21 healthy adults wore seven consumer-level activity monitors (Fitbit One, Fitbit Zip, Jawbone UP, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer and Withings Pulse) and two research-grade accelerometers/multi-sensor devices (BodyMedia SenseWear, and ActiGraph GT3X+) for 48-hours. Participants went about their daily life in free-living conditions during data collection. The validity of the consumer-level activity monitors relative to the research devices for step count, moderate to vigorous physical activity (MVPA), sleep and total daily energy expenditure (TDEE) was quantified using Bland-Altman analysis, median absolute difference and Pearson’s correlation. Results All consumer-level activity monitors correlated strongly (r > 0.8) with research-grade devices for step count and sleep time, but only moderately-to-strongly for TDEE (r = 0.74-0.81) and MVPA (r = 0.52-0.91). Median absolute differences were generally modest for sleep and steps (<10% of research device mean values for the majority of devices) moderate for TDEE (<30% of research device mean values), and large for MVPA (26-298%). Across the constructs examined, the Fitbit One, Fitbit Zip and Withings Pulse performed most strongly. Conclusions In free-living conditions, the consumer-level activity monitors showed strong validity for the measurement of steps and sleep duration, and moderate valid for measurement of TDEE and MVPA. Validity for each construct ranged widely between devices, with the Fitbit One, Fitbit Zip and Withings Pulse being the strongest performers.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications Inc. (Toronto, Canada )
                1439-4456
                1438-8871
                May 2016
                04 May 2016
                : 18
                : 5
                : e90
                Affiliations
                [1] 1Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit Loughborough University LoughboroughUnited Kingdom
                [2] 2National Centre for Sport and Exercise Medicine School of Sport, Exercise and Health Sciences Loughborough University LoughboroughUnited Kingdom
                [3] 3Diabetes Research Centre College of Medicine, Biological Sciences and Psychology University of Leicester LeicesterUnited Kingdom
                [4] 4Active Living and Public Health Institute of Sport, Exercise & Active Living (ISEAL) Victoria University MelbourneAustralia
                Author notes
                Corresponding Author: James P Sanders J.Sanders2@ 123456lboro.ac.uk
                Author information
                http://orcid.org/0000-0003-2103-0631
                http://orcid.org/0000-0002-5440-4305
                http://orcid.org/0000-0003-2060-5966
                http://orcid.org/0000-0001-6485-9330
                http://orcid.org/0000-0002-5724-5178
                http://orcid.org/0000-0002-7663-6895
                http://orcid.org/0000-0002-1236-6419
                Article
                v18i5e90
                10.2196/jmir.5373
                4871995
                27145905
                93fb00c1-b0bb-438f-9754-6814734cff90
                ©James P Sanders, Adam Loveday, Natalie Pearson, Charlotte Edwardson, Thomas Yates, Stuart JH Biddle, Dale W Esliger. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 04.05.2016.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 24 November 2015
                : 14 December 2015
                : 18 January 2016
                : 21 January 2016
                Categories
                Original Paper
                Original Paper

                Medicine
                sitting time,physical activity,measurement,feedback,activity monitor,scoping review
                Medicine
                sitting time, physical activity, measurement, feedback, activity monitor, scoping review

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